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Wu Z, Wang C, Li C, Xu N, Cao X, Chen S, Shi Y, He Y, Zhang P, Ji J. Integrated Computational Pipeline for the High-Throughput Discovery of Cell Adhesion Peptides. J Phys Chem Lett 2024; 15:3748-3756. [PMID: 38551401 DOI: 10.1021/acs.jpclett.4c00393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Cell adhesion peptides (CAPs) often play a critical role in tissue engineering research. However, the discovery of novel CAPs for diverse applications remains a challenging and time-intensive process. This study presents an efficient computational pipeline integrating sequence embeddings, binding predictors, and molecular dynamics simulations to expedite the discovery of new CAPs. A Pro2vec model, trained on vast CAP data sets, was built to identify RGD-similar tripeptide candidates. These candidates were further evaluated for their binding affinity with integrin receptors using the Mutabind2 machine learning model. Additionally, molecular dynamics simulations were applied to model receptor-peptide interactions and calculate their binding free energies, providing a quantitative assessment of the binding strength for further screening. The resulting peptide demonstrated performance comparable to that of RGD in endothelial cell adhesion and spreading experimental assays, validating the efficacy of the integrated computational pipeline.
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Affiliation(s)
- Zhiyu Wu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Cong Wang
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
| | - Chen Li
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Nan Xu
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Xiaoyong Cao
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
| | - Shengfu Chen
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yao Shi
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310058, China
| | - Yi He
- College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
- Institute of Zhejiang University-Quzhou, Quzhou 324000, China
- Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Peng Zhang
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory of Transvascular Implantation Devices, Qidi Road 456, Hangzhou 310058, China
| | - Jian Ji
- MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory of Transvascular Implantation Devices, Qidi Road 456, Hangzhou 310058, China
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Merz KM, Wei GW, Zhu F. Editorial: Machine Learning in Bio-cheminformatics. J Chem Inf Model 2024; 64:2125-2128. [PMID: 38587006 DOI: 10.1021/acs.jcim.4c00444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Affiliation(s)
- Kenneth M Merz
- Department of Chemistry, Michigan State University, Lansing 48824, Michigan, United States
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, Lansing 48824, Michigan, United States
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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Roy J, Roy K. Exploring the relationships between physiochemical properties of nanoparticles and cell damage to combat cancer growth using simple periodic table-based descriptors. BEILSTEIN JOURNAL OF NANOTECHNOLOGY 2024; 15:297-309. [PMID: 38505811 PMCID: PMC10949013 DOI: 10.3762/bjnano.15.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 02/23/2024] [Indexed: 03/21/2024]
Abstract
A comprehensive knowledge of the physical and chemical properties of nanomaterials (NMs) is necessary to design them effectively for regulated use. Although NMs are utilized in therapeutics, their cytotoxicity has attracted great attention. Nanoscale quantitative structure-property relationship (nano-QSPR) models can help in understanding the relationship between NMs and the biological environment and provide new ways for modeling the structural properties and bio-toxic effects of NMs. The goal of the study is to construct fully validated property-based models to extract relevant features for estimating and influencing the zeta potential and obtaining the toxicity profile regarding cell damage in the treatment of cancer cells. To achieve this, QSPR modeling was first performed with 18 metal oxide (MeOx) NMs to measure their materials properties using periodic table-based descriptors. The features obtained were later applied for zeta potential calculation (imputation for sparse data) for MeOx NMs that lack such information. To further clarify the influence of the zeta potential on cell damage, a QSPR model was developed with 132 MeOx NMs to understand the possible mechanisms of cell damage. The results showed that zeta potential, along with seven other descriptors, had the potential to influence oxidative damage through free radical accumulation, which could lead to changes in the survival rate of cancerous cells. The developed QSPR and quantitative structure-activity relationship models also give hints regarding safer design and toxicity assessment of MeOx NMs.
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Affiliation(s)
- Joyita Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India
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